Allemang, D., and Hendler, J. (2011). Semantic Web for the Working Ontologist: Effective Modeling in RDFS and Owl, Morgan Kaufmann, Burlington, MA.

Anderson, T., Schum, D., and Twining, W. (2005). Analysis of Evidence, Cambridge University Press, New York, NY.

Baring-Gould, W. S. (1967). The Annotated Sherlock Holmes, vols. I and II, Clarkson N. Potter, New York, NY.

Betham, J. (1810). An Introductory View of the Rationale of the Law of Evidence for Use by Non-Lawyers as Well as Lawyers (vi works 1–187 [Bowring edition, 1837–43] originally edited by James Mill circa 1810).

Black, H. C. (1968). Black's Law Dictionary, West Publishing Co., St. Paul, MN.

Boicu, M., Tecuci, G., Marcu, D. (2012). Rapid Argumentation Capture from Analysis Reports: The Case Study of Aum Shinrikyo, in Proceedings of the Seventh International Conference on Semantic Technologies for Intelligence, Defense, and Security – STIDS 2012, Fairfax, VA, October 23–26. ceur-ws.org/Vol-966/STIDS2012_T05_BoicuEtAl_RapidArgumentation.pdf (accessed November 25, 2015)

Boicu, M., Tecuci, G., Marcu, D., Bowman, M., Shyr, P., Ciucu, F., and Levcovici, C. (2000). Disciple-COA: From Agent Programming to Agent Teaching, in Proceedings of the Seventeenth International Conference on Machine Learning (ICML), Stanford, CA, June 29–July 2, Morgan Kaufman, Stanford, CA. lac.gmu.edu/publications/data/2000/2000_il-final.pdf (accessed November 25, 2015)

Bruce, J. B. (2008). Making Analysis More Reliable: Why Epistemology Matters to Intelligence, in George, R. Z., and Bruce, J. B., eds., Analyzing Intelligence: Origins, Obstacles, and Innovations, Georgetown University Press, Washington, DC, pp. 184–185.

Chantrell, G., ed. (2004). Oxford Dictionary of Word Histories, Oxford University Press, Oxford, UK.

Clemen, R. T. (1995). Making Hard Decisions, Duxbury Press, Belmont, CA.

Cohen, L. J. (1977). The Probable and the Provable, Clarendon Press, Oxford, UK.

Cohen, L. J. (1989). An Introduction to the Philosophy of Induction and Probability, Clarendon Press, Oxford, UK.

Dale, A. (2003). Most Honourable Remembrance: The Life and Work of Thomas Bayes, Springer-Verlag, New York, NY.

Danzig, R., Sageman, M., Leighton, T., Hough, L., Yuki, H., Kotani, R., and Hosford, Z. M. (2011). Aum Shinrikyo: Insights into How Terrorists Develop Biological and Chemical Weapons, Center for a New American Security, Washington, DC.

David, F. N. (1962). Gods, Games and Gambling, Griffin, London, UK.

Drogin, B. (2007). CURVEBALL: Spies, Lies, and the Con Man Who Caused a War, Random House, New York, NY.

George, R., and Bruce, J. B., eds. (2008). Analyzing Intelligence: Origins, Obstacles, and Innovations, Georgetown University Press, Washington, DC.

Heuer, R. J. (1999). Psychology of Intelligence Analysis, Center for the Study of Intelligence, Central Intelligence Agency, Washington, DC.

Heuer, R. J. (2008). Computer-Aided Analysis of Competing Hypotheses, in George, R. Z., and Bruce, J. B., eds., Analyzing Intelligence: Origins, Obstacles, and Innovations, Georgetown University Press, Washington, DC, pp. 251–265.

Hintikka, J. (1983). Sherlock Holmes Formalized, in Eco, U., and Sebeok, T., eds., The Sign of Three: Dupin, Holmes, Peirce, Indiana University Press, Bloomington, pp. 170–178.

Howe, M. (1999). Genius Explained, Cambridge University Press, Cambridge, UK.

Johnston, R. (2005). Analytic Culture in the U. S. Intelligence Community, Central Intelligence Agency, Washington, DC.

Kahneman, D., Slovic, P., and Tversky, A. (1982). Judgment under Uncertainty: Heuristics and Biases, Cambridge University Press, New York, NY.

Kahneman, D., and Tversky, A. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, vol. 185 (September 27), pp. 1124–1131.Google Scholar

Kent, S. (1994). Words of Estimated Probability, in Steury, D. P., ed., Sherman Kent and the Board of National Estimates: Collected Essays, Center for the Study of Intelligence, Central Intelligence Agency, Washington, DC, pp. 151–166.

Kolmogorov, A. N. (1956). Foundations of a Theory of Probability, (1933), Chelsea, New York, NY, pp. 3–4.

Kolmogorov, A. N. (1969). The Theory of Probability, in Aleksandrov, A. D., Kolmogorov, A. N., and Lavrentiev, M. A., eds., Mathematics: Its Content, Methods, and Meaning, vol. 2, ch. XI, MIT Press, Cambridge, MA, pp. 231–264.

Lempert, R. O., Gross, S. R., and Liebman, J. S. (2000). A Modern Approach to Evidence, ., West, St. Paul, MN, pp. 1146–1148.

Lindberg, C. A., ed. (2004). The Oxford American Writer's Thesaurus, Oxford University Press, Oxford, UK.

Marrin, S. (2011). Improving Intelligence Analysis: Bridging the Gap between Scholarship and Practice. Routlege, London, UK, and New York, NY.

Marrin, S., and Clemente, J. D. (2005). Improving Intelligence Analysis by Looking to the Medical Profession. International Journal of Intelligence and CounterIntelligence, vol. 18, no. 4, pp. 707–729.Google Scholar

Martin, D. C. (1980). Wilderness of Mirrors, Ballantine, New York, NY.

Meckl, S., Tecuci, G., Boicu, M., and Marcu, D. (2015). Towards an Operational Semantic Theory of Cyber Defense against Advanced Persistent Threats, in Laskey, K. B., Emmons, I., Costa, P. C. G., and Oltramari, A., eds., Proceedings of the Tenth International Conference on Semantic Technologies for Intelligence, Defense, and Security – STIDS 2015, pp. 58–65, Fairfax, VA, November 18–20. lac.gmu.edu/publications/2015/APT-LAC.pdf (accessed January 12, 2016)

Michalski, R. S., and Tecuci, G., eds. (1994). Machine Learning: A Multistrategy Approach, vol. IV, Morgan Kaufmann, San Mateo, CA.

Mueller, C. B., and Kirkpatrick, L. C. (2009). Federal Rules of Evidence, 2009 ed., West, St. Paul, MN.

Murphy, P. (2003). Evidence, Proof, and Facts: A Book of Sources, Oxford University Press, Oxford, UK, p. 1.

Negoita, C. V., and Ralescu, D. A. (1975). Applications of Fuzzy Sets to Systems Analysis, Wiley, New York.

Nilsson, N. J. (1971). Problem Solving Methods in Artificial Intelligence, McGraw-Hill, New York, NY.

Peirce, C. S. (1992 [1898]). Reasoning and the Logic of Things. The Cambridge Conferences Lectures of 1898, Ketner, K., ed., Harvard University Press, Cambridge, MA.

Peirce, C. S. (1955 [1901]). Abduction and Induction, 1901, in Philosophical Writings of Peirce, Buchler, J., ed., Dover, New York, NY, pp. 150–156.

Schum, D. A. (1987). Evidence and Inference for the Intelligence Analyst (2 volumes), University Press of America, Lanham, MD.

Schum, D. A. (1989). Knowledge, Probability, and Credibility, Journal of Behavioral Decision Making, vol. 2, pp. 39–62.Google Scholar

Schum, D. A. (1991). Jonathan Cohen and Thomas Bayes on the Analysis of Chains of Reasoning, in Eells, E., and Maruszewski, T., eds., Probability and Rationality: Studies on L. Jonathan Cohen's Philosophy of Science, Editions Rodopi, Amsterdam, Netherlands, pp. 99–145.

Schum, D. A. (1999). Marshaling Thoughts and Evidence during Fact Investigation. South Texas Law Review, vol. 40, no. 2 (Summer), pp. 401–454.Google Scholar

Schum, D. A. (1994 [2001a]). The Evidential Foundations of Probabilistic Reasoning, Northwestern University Press, Evanston, IL.

Schum, D. A. (2001b). Species of Abductive Reasoning in Fact Investigation in Law. Cardozo Law Review, vol. 22, nos. 5–6, pp. 1645–1681.Google Scholar

Schum, D. A. (2011). Classifying Forms and Combinations of Evidence: Necessary in a Science of Evidence, in Dawid, P., Twining, W., and Vasilaki, , eds., Evidence, Inference and Enquiry, British Academy, Oxford University Press, Oxford, UK, pp. 11–36.

Schum, D. A., and Morris, J. (2007). Assessing the Competence and Credibility of Human Sources of Evidence: Contributions from Law and Probability, Law, Probability and Risk, vol. 6, pp. 247–274.Google Scholar

Schum, D. A., Tecuci, G., and Boicu, M. (2009). Analyzing Evidence and Its Chain of Custody: A Mixed-Initiative Computational Approach, International Journal of Intelligence and Counterintelligence, vol. 22, pp. 298–319. lac.gmu.edu/publications/2009/Schum et al - Chain of Custody.pdf (accessed November 25, 2015)Google Scholar

Shafer, G. (1976). A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ.

Shafer, G. (1988). Combining AI and OR. University of Kansas School of Business Working Paper No. 195, April.

Simonite, T. (2013). Bill Gates: Software Assistants Could Help Solve Global Problems, MIT Technology Review, July 16. technologyreview.com/news/517171/bill-gates-software-assistants-could-help-solve-global-problems/ (accessed November 25, 2015)

Tecuci, G. (1988). Disciple: A Theory, Methodology and System for Learning Expert Knowledge, Thèse de Docteur en Science, University of Paris-South. lac.gmu.edu/publications/1988/TecuciG_PhD_Thesis.pdf (accessed November 25, 2015)

Tecuci, G. (1993). Plausible Justification Trees: A Framework for the Deep and Dynamic Integration of Learning Strategies. Machine Learning Journal, vol. 11, pp. 237–261. lac.gmu.edu/publications/1993/TecuciG_Plausible_Justification_Trees.pdf (accessed November 25, 2015)Google Scholar

Tecuci, G. (1998). Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case Studies. Academic Press, London, UK. lac.gmu.edu/publications/1998/TecuciG_Building_Intelligent_Agents/default.htm (accessed November 25, 2015)

Tecuci, G., and Keeling, H. (1999). Developing an Intelligent Educational Agent with Disciple. International Journal of Artificial Intelligence in Education, vol. 10, no. 3–4. lac.gmu.edu/publications/1999/TecuciG_Intelliget_Educational_Agent.pdf (accessed November 25, 2015)Google Scholar

Tecuci, G., and Kodratoff, Y., eds. (1995). Machine Learning and Knowledge Acquisition: Integrated Approaches, Academic Press, London, UK. lac.gmu.edu/publications/1995/TecuciG_MLKA_Integrated_Approaches.pdf (accessed November 25, 2015)

Tecuci, G., and Michalski, R. S. (1991). A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications, in Birnbaum, L., and Collins, G., eds., Machine Learning: Proceedings of the Eighth International Conference, pp. 549–553, Chicago, IL, June, Morgan Kaufmann, San Mateo, CA. lac.gmu.edu/publications/1991/TecuciG_Multistrategy_Learning_Method.pdf (accessed November 25, 2015)

Tecuci, G., Boicu, M., Wright, K., Lee, S. W., Marcu, D., and Bowman, M. (1999). An Integrated Shell and Methodology for Rapid Development of Knowledge-Based Agents, in Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), Orlando, FL, July 18–22, AAAI Press, Menlo Park, CA. lac.gmu.edu/publications/data/1999/ismrdkba.pdf (accessed November 25, 2015).

Tecuci, G., Boicu, M., Wright, K., Lee, S. W., Marcu, D., and Bowman, M. (2000). A Tutoring Based Approach to the Development of Intelligent Agents, in Teodorescu, H. N., Mlynek, D., Kandel, A., and Zimmermann, H. J., eds., Intelligent Systems and Interfaces, Kluwer Academic Press, Boston, MA. lac.gmu.edu/publications/data/2000/2000_Disciple-Planning.pdf (accessed November 25, 2015)

Tecuci, G., Boicu, M., Bowman, M., and Marcu, D., with commentary by Burke, M. (2001). An Innovative Application from the DARPA Knowledge Bases Programs: Rapid Development of a Course of Action Critiquer, AI Magazine, vol. 22, no. 2, pp. 43–61. lac.gmu.edu/publications/2001/TecuciG_Disciple_COA_IAAI.pdf (accessed November 25, 2015)Google Scholar

Tecuci, G., Boicu, M., Marcu, D., Stanescu, B., Boicu, C., Comello, J., Lopez, A., Donlon, J., and Cleckner, W. (2002a) Development and Deployment of a Disciple Agent for Center of Gravity Analysis, in Proceedings of the Eighteenth National Conference of Artificial Intelligence and the Fourteenth Conference on Innovative Applications of Artificial Intelligence, AAAI-02/IAAI-02, pp. 853–860, Edmonton, Alberta, Canada, AAAI Press, Menlo Park, CA/MIT Press, Boston, MA. lac.gmu.edu/publications/data/2002/dddacga.pdf (accessed November 25, 2015)

Tecuci, G., Boicu, M., Marcu, D., Stanescu, B., Boicu, C., Comello, J. (2002b). Training and Using Disciple Agents: A Case Study in the Military Center of Gravity Analysis Domain, AI Magazine, vol. 24, no. 4, pp. 51–68, AAAI Press, Menlo Park, CA. lac.gmu.edu/publications/2002/TecuciG_Disciple_COG_IAAI.pdf (accessed November 25, 2015)Google Scholar

Tecuci, G., Boicu, M., Ayers, C., and Cammons, D. (2005a). Personal Cognitive Assistants for Military Intelligence Analysis: Mixed-Initiative Learning, Tutoring, and Problem Solving, in Proceedings of the First International Conference on Intelligence Analysis, McLean, VA, May 2–6. lac.gmu.edu/publications/data/2005/Tecuci-Disciple-LTA.pdf (accessed November 25, 2015)

Tecuci, G., Boicu, M., Boicu, C., Marcu, D., Stanescu, B., and Barbulescu, M. (2005b). The Disciple-RKF Learning and Reasoning Agent. Computational Intelligence, vol. 21, no. 4, pp. 462–479. lac.gmu.edu/publications/2005/TecuciG_Disciple_RKF_CI.pdf (accessed November 25, 2015)Google Scholar

Tecuci, G., Boicu, M., and Comello, J. (2008b). Agent-Assisted Center of Gravity Analysis, CD with Disciple-COG and Lecture Notes used in courses at the U.S. Army War College and Air War College, George Mason University Press, Fairfax, VA. lac.gmu.edu/cog-book/ (accessed November 25, 2015)

Tecuci, G., Boicu, M., and Cox, M. T. (2007a). Seven Aspects of Mixed-Initiative Reasoning: An Introduction to the Special Issue on Mixed-Initiative Assistants. AI Magazine, vol. 28, no. 2, pp. 11–18. www.aaai.org/ojs/index.php/aimagazine/issue/view/174/showToc (accessed May 29, 2015)Google Scholar

Tecuci, G., Boicu, M., Marcu, D., Boicu, C., Barbulescu, M., Ayers, C., and Cammons, D. (2007b). Cognitive Assistants for Analysts. Journal of Intelligence Community Research and Development (JICRD). Also published in Auger, John, and Wimbish, William, eds., (2007) Proteus Futures Digest: A Compilation of Selected Works Derived from the 2006 Proteus Workshop, Joint publication of the National Intelligence University, Office of the Director of National Intelligence, Tysons Corner, VA, and U.S. Army War College Center for Strategic Leadership, Carlisle, PA, pp. 303–329. lac.gmu.edu/publications/2007/TecuciG_Cognitive_Assistants.pdf (accessed November 25, 2015)

Tecuci, G., Boicu, M., Marcu, D., Boicu, C., and Barbulescu, M. (2008a). Disciple-LTA: Learning, Tutoring and Analytic Assistance, Journal of Intelligence Community Research and Development (JICRD), July. lac.gmu.edu/publications/2008/Disciple-LTA08.pdf (accessed November 25, 2015)

Tecuci, G., Boicu, M., Marcu, D., and Schum, D. (2013b). How Learning Enables Intelligence Analysts to Rapidly Develop Practical Cognitive Assistants, in Proceedings of the 12th International Conference on Machine Learning and Applications (ICMLA'13), Miami, FL, December 4–7. lac.gmu.edu/publications/2013/LAC-ICMLA-13.pdf (accessed November 25, 2015)

Tecuci, G., Marcu, D., Boicu, M., Schum, D. A., and Russell, K. (2011a). Computational Theory and Cognitive Assistant for Intelligence Analysis, in Costa, P. C. G., and Laskey, K., eds., Proceedings of the Sixth International Conference on Semantic Technologies for Intelligence, Defense, and Security –STIDS 2011, pp. 68–75, Fairfax, VA, November 16–18. CEUR Workshop Proceedings, Fairfax, VA. ceur-ws.org/Vol-808/STIDS2011_CR_T9_TecuciEtAl.pdf (accessed May 29, 2015)

Tecuci, G., Marcu, D., Boicu, M., and Schum, D. A. (2016). Knowledge Engineering: Building Cognitive Assistants for Evidence-based Reasoning, Cambridge University Press, New York, NY.

Tecuci, G., Boicu, M., Marcu, D., Barbulescu, M., Boicu, C., Le, V., and Hajduk, T. (2008c). Teaching Virtual Experts for Multi-Domain Collaborative Planning. Journal of Software, vol. 3, no. 3 (March), pp. 38–59. lac.gmu.edu/publications/2008/TecuciG_Disciple_VE_JS.pdf (accessed November 25, 2015)Google Scholar

Tecuci, G., Schum, D. A., Boicu, M., Marcu, D., Hamilton, B., and Wible, B. (2010). Teaching Intelligence Analysis with TIACRITIS, American Intelligence Journal, vol. 28, no. 2 (December), pp. 50–65. lac.gmu.edu/publications/2010/Tiacritis-AIJ.pdf (accessed November 25, 2015)Google Scholar

Tecuci, G., Schum, D. A., Boicu, M., and Marcu, D. (2011b). Introduction to Intelligence Analysis: A Hands-on Approach with TIACRITIS, Learning Agents Center, George Mason University, Fairfax, VA. lac.gmu.edu/publications/2011/TecuciG_Introduction_Intelligence_Analysis.pdf (accessed November 25, 2015)

Tecuci, G., Schum, D. A., Marcu, D., and Boicu, M. (2013a). Recognizing and Countering Biases in Intelligence Analysis with TIACRITIS, in Laskey, K. B., Emmons, I., and Costa, P. C. G., eds., Proceedings of the Eighth International Conference on Semantic Technologies for Intelligence, Defense, and Security – STIDS 2013, Fairfax, VA, November 13–14. CEUR Workshop Proceedings, Fairfax, VA. ceur-ws.org/Vol-1097/STIDS2013_T04_TecuciEtAl.pdf (accessed May 29, 2015)

Tecuci, G., Schum, D. A., Marcu, D., and Boicu, M. (2014). Computational Approach and Cognitive Assistant for Evidence-based Reasoning in Intelligence Analysis. International Journal of Intelligent Defence Support Systems, vol. 5, no. 2, pp. 146–172. lac.gmu.edu/publications/2014/Disciple-CD-IJIDSS.pdf (accessed November 25, 2015)Google Scholar

Toulmin, S. E. (1963). The Uses of Argument, Cambridge University Press, Cambridge, UK.

Van Gelder, T. J. (2007). The Rationale for Rationale, Law, Probability and Risk, vol. 6, pp. 23–42. https://sites.google.com/site/timvangelder/publications-1/therationaleforrationale (accessed February 11, 2016)Google Scholar

Wigmore, J. H. (1913). The Problem of Proof, Illinois Law Review, vol. 8, no. 2, pp. 77–103.Google Scholar

Wigmore, J. H. (1937). The Science of Judicial Proof: As Given by Logic, Psychology, and General Experience and Illustrated in Judicial Trials, Little, Brown, Boston, MA.

Wigmore, J. H. (1940). Treatise on the Anglo-American System of Evidence in Trials at Common Law (usually referred to as Wigmore on Evidence), vol. 7, ., Little, Brown, Boston, MA..

W3C (2015). Semantic Web. www.w3.org/standards/semanticweb/ (accessed November 25, 2015)

Zadeh, L. (1983). The Role of Fuzzy Logic in the Management of Uncertainty in Expert Systems, Fuzzy Sets and Systems, vol. 11, pp. 199–227.Google Scholar